Overview

Dataset statistics

Number of variables10
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.9 KiB
Average record size in memory81.3 B

Variable types

Numeric10

Alerts

AIC.sr is highly correlated with AIC.gam and 7 other fieldsHigh correlation
AIC.gam is highly correlated with AIC.sr and 7 other fieldsHigh correlation
AIC.tree is highly correlated with AIC.sr and 7 other fieldsHigh correlation
BIC.sr is highly correlated with AIC.sr and 7 other fieldsHigh correlation
BIC.gam is highly correlated with AIC.sr and 7 other fieldsHigh correlation
BIC.tree is highly correlated with AIC.sr and 7 other fieldsHigh correlation
EIC.sr is highly correlated with AIC.sr and 7 other fieldsHigh correlation
EIC.gam is highly correlated with AIC.sr and 7 other fieldsHigh correlation
EIC.tree is highly correlated with AIC.sr and 7 other fieldsHigh correlation
df_index is uniformly distributed Uniform
df_index has unique values Unique
AIC.sr has unique values Unique
AIC.gam has unique values Unique
AIC.tree has unique values Unique
BIC.sr has unique values Unique
BIC.gam has unique values Unique
BIC.tree has unique values Unique
EIC.sr has unique values Unique
EIC.gam has unique values Unique
EIC.tree has unique values Unique

Reproduction

Analysis started2022-11-15 15:12:21.793506
Analysis finished2022-11-15 15:12:36.244467
Duration14.45 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.5
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2022-11-16T00:12:36.275096image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.95
Q125.75
median50.5
Q375.25
95-th percentile95.05
Maximum100
Range99
Interquartile range (IQR)49.5

Descriptive statistics

Standard deviation29.01149198
Coefficient of variation (CV)0.5744849896
Kurtosis-1.2
Mean50.5
Median Absolute Deviation (MAD)25
Skewness0
Sum5050
Variance841.6666667
MonotonicityStrictly increasing
2022-11-16T00:12:36.392683image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
1.0%
641
 
1.0%
741
 
1.0%
731
 
1.0%
721
 
1.0%
711
 
1.0%
701
 
1.0%
691
 
1.0%
681
 
1.0%
671
 
1.0%
Other values (90)90
90.0%
ValueCountFrequency (%)
11
1.0%
21
1.0%
31
1.0%
41
1.0%
51
1.0%
61
1.0%
71
1.0%
81
1.0%
91
1.0%
101
1.0%
ValueCountFrequency (%)
1001
1.0%
991
1.0%
981
1.0%
971
1.0%
961
1.0%
951
1.0%
941
1.0%
931
1.0%
921
1.0%
911
1.0%

AIC.sr
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean278.1988902
Minimum260.2504978
Maximum295.8652095
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2022-11-16T00:12:36.539099image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum260.2504978
5-th percentile267.0359824
Q1273.625188
median278.8999234
Q3282.8616814
95-th percentile288.803209
Maximum295.8652095
Range35.61471173
Interquartile range (IQR)9.236493429

Descriptive statistics

Standard deviation6.860821506
Coefficient of variation (CV)0.02466157037
Kurtosis-0.1605139883
Mean278.1988902
Median Absolute Deviation (MAD)4.440907544
Skewness-0.1417075916
Sum27819.88902
Variance47.07087174
MonotonicityNot monotonic
2022-11-16T00:12:36.681532image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
275.58507011
 
1.0%
276.80567441
 
1.0%
272.24563321
 
1.0%
282.84238351
 
1.0%
280.61845021
 
1.0%
285.42798741
 
1.0%
284.24114981
 
1.0%
270.64849031
 
1.0%
267.0982241
 
1.0%
283.03856841
 
1.0%
Other values (90)90
90.0%
ValueCountFrequency (%)
260.25049781
1.0%
262.97225361
1.0%
264.343251
1.0%
265.65184971
1.0%
265.8533911
1.0%
267.0982241
1.0%
267.91569571
1.0%
268.10183191
1.0%
268.58730331
1.0%
268.8786421
1.0%
ValueCountFrequency (%)
295.86520951
1.0%
291.74495371
1.0%
291.2757881
1.0%
289.73947451
1.0%
289.40554811
1.0%
288.77150691
1.0%
288.03347241
1.0%
287.62889221
1.0%
287.17582861
1.0%
287.13003881
1.0%

AIC.gam
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean278.0807724
Minimum260.5253252
Maximum295.8671476
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2022-11-16T00:12:36.849062image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum260.5253252
5-th percentile266.064329
Q1273.4581179
median278.6164003
Q3282.9372537
95-th percentile288.5111608
Maximum295.8671476
Range35.34182241
Interquartile range (IQR)9.479135784

Descriptive statistics

Standard deviation6.944103718
Coefficient of variation (CV)0.02497153492
Kurtosis-0.2460663717
Mean278.0807724
Median Absolute Deviation (MAD)4.879455763
Skewness-0.09072519657
Sum27808.07724
Variance48.22057645
MonotonicityNot monotonic
2022-11-16T00:12:36.957035image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
275.7889271
 
1.0%
277.2239691
 
1.0%
272.19740861
 
1.0%
282.84608141
 
1.0%
278.4821971
 
1.0%
284.84605631
 
1.0%
284.24475821
 
1.0%
270.79638961
 
1.0%
267.02624621
 
1.0%
282.19961841
 
1.0%
Other values (90)90
90.0%
ValueCountFrequency (%)
260.52532521
1.0%
262.98019331
1.0%
264.76231951
1.0%
265.85803791
1.0%
265.89072571
1.0%
266.0734661
1.0%
267.02624621
1.0%
267.78765471
1.0%
268.10790921
1.0%
268.59197281
1.0%
ValueCountFrequency (%)
295.86714761
1.0%
292.35413081
1.0%
291.27344131
1.0%
290.08369781
1.0%
289.74450961
1.0%
288.44624771
1.0%
288.03815951
1.0%
287.62980381
1.0%
287.15842761
1.0%
287.13269551
1.0%

AIC.tree
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean282.5672749
Minimum262.2504978
Maximum313.9584683
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2022-11-16T00:12:37.118218image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum262.2504978
5-th percentile272.2492272
Q1277.5598852
median281.9395619
Q3286.8856774
95-th percentile292.8065279
Maximum313.9584683
Range51.70797053
Interquartile range (IQR)9.325792209

Descriptive statistics

Standard deviation7.373648348
Coefficient of variation (CV)0.02609519574
Kurtosis2.604648585
Mean282.5672749
Median Absolute Deviation (MAD)4.798098647
Skewness0.6303862174
Sum28256.72749
Variance54.37068997
MonotonicityNot monotonic
2022-11-16T00:12:37.205416image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
284.95000841
 
1.0%
285.13907621
 
1.0%
274.24563321
 
1.0%
284.84238351
 
1.0%
292.64868141
 
1.0%
287.42798741
 
1.0%
286.24114981
 
1.0%
281.56954641
 
1.0%
275.2856871
 
1.0%
285.03856841
 
1.0%
Other values (90)90
90.0%
ValueCountFrequency (%)
262.25049781
1.0%
266.343251
1.0%
270.8786421
1.0%
271.61755291
1.0%
272.10907941
1.0%
272.25660341
1.0%
272.48199761
1.0%
273.0410121
1.0%
273.25427851
1.0%
274.24563321
1.0%
ValueCountFrequency (%)
313.95846831
1.0%
300.63568411
1.0%
294.9121541
1.0%
293.74495371
1.0%
293.2757881
1.0%
292.781831
1.0%
292.64868141
1.0%
292.14306411
1.0%
291.73947451
1.0%
291.40554811
1.0%

BIC.sr
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean288.6195709
Minimum270.6711785
Maximum306.2858902
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2022-11-16T00:12:37.345334image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum270.6711785
5-th percentile277.4566631
Q1284.0458687
median289.3206041
Q3293.2823621
95-th percentile299.2238897
Maximum306.2858902
Range35.61471173
Interquartile range (IQR)9.236493429

Descriptive statistics

Standard deviation6.860821506
Coefficient of variation (CV)0.02377115829
Kurtosis-0.1605139883
Mean288.6195709
Median Absolute Deviation (MAD)4.440907544
Skewness-0.1417075916
Sum28861.95709
Variance47.07087174
MonotonicityNot monotonic
2022-11-16T00:12:37.465071image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
286.00575091
 
1.0%
287.22635511
 
1.0%
282.66631391
 
1.0%
293.26306431
 
1.0%
291.03913091
 
1.0%
295.84866811
 
1.0%
294.66183051
 
1.0%
281.0691711
 
1.0%
277.51890481
 
1.0%
293.45924911
 
1.0%
Other values (90)90
90.0%
ValueCountFrequency (%)
270.67117851
1.0%
273.39293431
1.0%
274.76393081
1.0%
276.07253051
1.0%
276.27407181
1.0%
277.51890481
1.0%
278.33637641
1.0%
278.52251271
1.0%
279.0079841
1.0%
279.29932281
1.0%
ValueCountFrequency (%)
306.28589021
1.0%
302.16563451
1.0%
301.69646881
1.0%
300.16015521
1.0%
299.82622891
1.0%
299.19218771
1.0%
298.45415311
1.0%
298.04957291
1.0%
297.59650931
1.0%
297.55071951
1.0%

BIC.gam
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean291.0494894
Minimum271.9494375
Maximum307.6500715
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2022-11-16T00:12:37.593277image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum271.9494375
5-th percentile279.0001161
Q1285.735108
median291.1177584
Q3296.5085385
95-th percentile304.7834436
Maximum307.6500715
Range35.70063392
Interquartile range (IQR)10.77343044

Descriptive statistics

Standard deviation7.602313771
Coefficient of variation (CV)0.02612034739
Kurtosis-0.2061799269
Mean291.0494894
Median Absolute Deviation (MAD)5.398685309
Skewness0.01989757505
Sum29104.94894
Variance57.79517468
MonotonicityNot monotonic
2022-11-16T00:12:37.704594image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
288.83719691
 
1.0%
289.61766581
 
1.0%
285.75114291
 
1.0%
293.27311281
 
1.0%
294.00604161
 
1.0%
299.26460481
 
1.0%
294.6724191
 
1.0%
284.33968511
 
1.0%
282.54304171
 
1.0%
301.06198031
 
1.0%
Other values (90)90
90.0%
ValueCountFrequency (%)
271.94943751
1.0%
273.4128051
1.0%
276.28750031
1.0%
277.97213091
1.0%
278.53804961
1.0%
279.02443541
1.0%
279.4756951
1.0%
280.15970851
1.0%
280.53687681
1.0%
281.79280691
1.0%
ValueCountFrequency (%)
307.65007151
1.0%
307.27058611
1.0%
306.29420841
1.0%
306.0138141
1.0%
305.89503771
1.0%
304.72493861
1.0%
302.54086281
1.0%
302.45814511
1.0%
301.77931151
1.0%
301.70065471
1.0%

BIC.tree
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean300.5950526
Minimum275.2763487
Maximum358.2463614
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2022-11-16T00:12:37.807226image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum275.2763487
5-th percentile285.2750781
Q1291.5786293
median297.9068302
Q3304.9694233
95-th percentile323.5698146
Maximum358.2463614
Range82.97001277
Interquartile range (IQR)13.39079404

Descriptive statistics

Standard deviation13.40202533
Coefficient of variation (CV)0.04458498307
Kurtosis3.398920181
Mean300.5950526
Median Absolute Deviation (MAD)6.634692589
Skewness1.455243282
Sum30059.50526
Variance179.6142829
MonotonicityNot monotonic
2022-11-16T00:12:37.936202image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
313.60688041
 
1.0%
313.79594821
 
1.0%
287.27148411
 
1.0%
297.86823441
 
1.0%
321.30555351
 
1.0%
300.45383831
 
1.0%
299.26700071
 
1.0%
310.22641841
 
1.0%
303.9425591
 
1.0%
298.06441931
 
1.0%
Other values (90)90
90.0%
ValueCountFrequency (%)
275.27634871
1.0%
279.3691011
1.0%
283.9044931
1.0%
284.64340381
1.0%
285.13493041
1.0%
285.28245431
1.0%
285.50784851
1.0%
286.06686291
1.0%
286.28012941
1.0%
287.27148411
1.0%
ValueCountFrequency (%)
358.24636141
1.0%
344.92357731
1.0%
333.51386431
1.0%
325.36118591
1.0%
323.58479681
1.0%
323.56902611
1.0%
321.4387021
1.0%
321.30555351
1.0%
320.79993621
1.0%
320.02202391
1.0%

EIC.sr
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean270.2822593
Minimum252.1781644
Maximum287.7265018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2022-11-16T00:12:38.062091image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum252.1781644
5-th percentile259.4612687
Q1266.2814803
median270.9640921
Q3274.9057043
95-th percentile280.4029123
Maximum287.7265018
Range35.54833746
Interquartile range (IQR)8.624224034

Descriptive statistics

Standard deviation6.868284627
Coefficient of variation (CV)0.02541152588
Kurtosis-0.1644897622
Mean270.2822593
Median Absolute Deviation (MAD)4.394639781
Skewness-0.1399337999
Sum27028.22593
Variance47.17333372
MonotonicityNot monotonic
2022-11-16T00:12:38.174519image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
267.68310041
 
1.0%
269.01175031
 
1.0%
264.74022071
 
1.0%
274.83006361
 
1.0%
272.28978541
 
1.0%
277.60893831
 
1.0%
276.13880861
 
1.0%
262.26039141
 
1.0%
259.53295671
 
1.0%
275.28211271
 
1.0%
Other values (90)90
90.0%
ValueCountFrequency (%)
252.17816441
1.0%
255.20020671
1.0%
256.21728861
1.0%
257.75227011
1.0%
258.09919781
1.0%
259.53295671
1.0%
259.90100371
1.0%
260.36303241
1.0%
260.56436081
1.0%
260.99661361
1.0%
ValueCountFrequency (%)
287.72650181
1.0%
284.07816931
1.0%
283.59638831
1.0%
281.73562911
1.0%
281.62192261
1.0%
280.33875391
1.0%
280.13390411
1.0%
279.53853781
1.0%
279.43661081
1.0%
279.31294531
1.0%

EIC.gam
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean268.2217709
Minimum251.562261
Maximum288.0374331
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2022-11-16T00:12:38.324196image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum251.562261
5-th percentile255.3650601
Q1262.7842924
median267.7315183
Q3273.5640309
95-th percentile279.598027
Maximum288.0374331
Range36.47517212
Interquartile range (IQR)10.77973857

Descriptive statistics

Standard deviation7.26651085
Coefficient of variation (CV)0.0270914282
Kurtosis-0.1756651113
Mean268.2217709
Median Absolute Deviation (MAD)5.632349208
Skewness0.06797302029
Sum26822.17709
Variance52.80217993
MonotonicityNot monotonic
2022-11-16T00:12:38.533911image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
266.18175591
 
1.0%
267.4538181
 
1.0%
262.06098471
 
1.0%
275.06761211
 
1.0%
266.10039431
 
1.0%
273.76868321
 
1.0%
276.12637051
 
1.0%
260.14972151
 
1.0%
255.16158341
 
1.0%
267.69830631
 
1.0%
Other values (90)90
90.0%
ValueCountFrequency (%)
251.5622611
1.0%
253.24766851
1.0%
253.28974231
1.0%
255.07061731
1.0%
255.16158341
1.0%
255.37576941
1.0%
257.39845161
1.0%
257.97356041
1.0%
258.82882391
1.0%
259.78252241
1.0%
ValueCountFrequency (%)
288.03743311
1.0%
284.00536841
1.0%
282.66736441
1.0%
281.60826861
1.0%
279.96100161
1.0%
279.57892311
1.0%
279.26588361
1.0%
277.92001041
1.0%
277.57134131
1.0%
277.02884381
1.0%

EIC.tree
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean268.8109968
Minimum245.543102
Maximum284.0817686
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2022-11-16T00:12:38.678618image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum245.543102
5-th percentile253.7215329
Q1264.7175851
median269.1901277
Q3274.6438587
95-th percentile280.1432804
Maximum284.0817686
Range38.5386666
Interquartile range (IQR)9.926273591

Descriptive statistics

Standard deviation8.030405516
Coefficient of variation (CV)0.02987379836
Kurtosis0.1404208199
Mean268.8109968
Median Absolute Deviation (MAD)5.284767789
Skewness-0.5436749184
Sum26881.09968
Variance64.48741274
MonotonicityNot monotonic
2022-11-16T00:12:38.825378image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
263.05186731
 
1.0%
263.34610721
 
1.0%
264.7396491
 
1.0%
274.83047071
 
1.0%
270.31932311
 
1.0%
277.61384341
 
1.0%
276.13820111
 
1.0%
259.17997751
 
1.0%
253.7241761
 
1.0%
275.28513211
 
1.0%
Other values (90)90
90.0%
ValueCountFrequency (%)
245.5431021
1.0%
247.30049571
1.0%
252.17939531
1.0%
253.55854231
1.0%
253.67131471
1.0%
253.7241761
1.0%
254.32587911
1.0%
254.90978651
1.0%
255.19093011
1.0%
256.2180421
1.0%
ValueCountFrequency (%)
284.08176861
1.0%
283.597041
1.0%
281.73679821
1.0%
281.6236911
1.0%
280.33327231
1.0%
280.13328081
1.0%
279.81952951
1.0%
279.5480891
1.0%
279.43685911
1.0%
279.31272741
1.0%

Interactions

2022-11-16T00:12:35.594567image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:25.171871image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:27.699809image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:28.878421image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:29.910301image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:30.903492image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:32.271638image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:33.263988image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:34.171021image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:35.106858image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:35.630463image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:25.289929image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:27.814878image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:28.995872image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:30.013795image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:31.023692image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:32.341637image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:33.337937image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:34.240636image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:35.180706image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:35.682793image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:26.859626image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:27.939164image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:29.116565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:30.143461image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:31.433229image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:32.442829image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:33.408528image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:34.313054image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:35.280393image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:35.720074image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:26.934042image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:28.054412image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:29.200949image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:30.265513image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:31.524443image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:32.553695image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:33.472206image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:34.404244image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:35.325985image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:35.766273image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:27.014182image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:28.172656image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:29.301297image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:30.353352image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:31.647325image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:32.671860image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:33.576925image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:34.488806image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:35.362778image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:35.838048image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:27.095886image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:28.290289image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:29.376738image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:30.452071image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:31.772053image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:32.766979image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:33.688068image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:34.593870image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:35.402787image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:35.945304image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:27.345759image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:28.423262image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:29.446599image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:30.522587image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:31.885161image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:32.875080image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:33.788849image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:34.664009image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:35.440077image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:35.981780image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:27.410887image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:28.516797image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:29.533655image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:30.584411image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:31.998125image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:32.951898image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:33.896233image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:34.728431image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:35.475139image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:36.028795image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:27.483979image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:28.629346image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:29.660417image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:30.652918image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:32.086791image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:33.029584image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:33.986224image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:34.808178image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:35.516257image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:36.074380image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:27.569253image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:28.751293image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:29.780979image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:30.784888image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:32.194139image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:33.123290image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:34.085447image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:34.988463image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-16T00:12:35.555166image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-11-16T00:12:38.983577image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-16T00:12:39.157320image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-16T00:12:39.322025image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-16T00:12:39.493345image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-16T00:12:39.674328image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-16T00:12:36.141319image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-16T00:12:36.213480image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexAIC.srAIC.gamAIC.treeBIC.srBIC.gamBIC.treeEIC.srEIC.gamEIC.tree
01275.585070275.788927284.950008286.005751288.837197313.606880267.683100266.181756263.051867
12278.585158279.364691280.585158289.005838295.254038293.611008269.298797265.658174269.293823
23274.486250274.292065281.799947284.906930289.796173310.456819267.502656263.000984260.822241
34275.716443275.722376277.716443286.137124286.153176290.742294267.344768267.161657267.342285
45277.646586278.068072279.646586288.067267290.221637292.672437269.766009268.682201269.765862
56281.621232281.851985283.621232292.041913295.617241296.647083273.851531270.859279273.850595
67281.693925280.519627283.693925292.114606294.333802296.719776273.925202270.215227273.928364
78273.487037273.830538275.487037283.907717285.878716288.512888265.677425264.790503265.676750
89270.109079270.798104272.109079280.529760284.766641285.134930261.393162259.890653261.390107
910279.071772279.583297286.896780289.492453291.845068315.553652271.295411270.363189265.120457

Last rows

df_indexAIC.srAIC.gamAIC.treeBIC.srBIC.gamBIC.treeEIC.srEIC.gamEIC.tree
9091281.776976281.782264300.635684292.197656292.213116344.923577273.358562273.385691266.216837
9192268.878642269.238283270.878642279.299323281.792807283.904493260.564361258.828824260.564500
9293274.431782273.584247276.431782284.852463288.823689289.457633267.050120262.857766267.052808
9394262.972254262.980193281.073293273.392934273.412805325.361186255.200207255.375769247.300496
9495279.265228279.418876281.265228289.685909292.360585294.291079271.412330269.524249271.411814
9596277.029982277.574038279.029982287.450663289.928648292.055833269.186302268.274706269.188045
9697275.127920276.026444277.127920285.548601289.171751290.153771266.706499265.171924266.703359
9798289.739474289.744510291.739474300.160155300.173606304.765325281.735629281.608269281.736798
9899280.079177275.948991282.079177290.499858292.741596295.105028272.954764263.614564272.957425
99100275.222793275.636894277.222793285.643474288.972761290.248644267.495742266.359048267.495938